Cover Image

Explaining GNN Explanations with Edge Gradients

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	He, Jesse, Rafiey, Akbar, Mishne, Gal, Wang, Yusu
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Elucidating Flow Matching ODE Dynamics with Respect to Data Geometries and Denoisers
by: Wan, Zhengchao, et al.
Published: (2024)

The Numerical Stability of Hyperbolic Representation Learning
by: Mishne, Gal, et al.
Published: (2022)

MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning
by: He, Jesse, et al.
Published: (2026)

Decomposable Submodular Maximization in Federated Setting
by: Rafiey, Akbar
Published: (2024)

Comparing Graph Transformers via Positional Encodings
by: Black, Mitchell, et al.
Published: (2024)

Learning Cartesian Product Graphs with Laplacian Constraints
by: Shi, Changhao, et al.
Published: (2024)

Learning Kronecker-Structured Graphs from Smooth Signals
by: Shi, Changhao, et al.
Published: (2025)

Graph Laplacian Learning with Exponential Family Noise
by: Shi, Changhao, et al.
Published: (2023)

GNN Explanations that do not Explain and How to find Them
by: Azzolin, Steve, et al.
Published: (2026)

Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization
by: Kohli, Dhruv, et al.
Published: (2025)

Deep and shallow data science for multi-scale optical neuroscience
by: Mishne, Gal, et al.
Published: (2024)

SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
by: Mudrik, Noga, et al.
Published: (2023)

EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
by: Lu, Shengyao, et al.
Published: (2024)

Cluster and then Embed: A Modular Approach for Visualization
by: Coda, Elizabeth, et al.
Published: (2025)

Knowing What You Cannot Explain: Learning to Reject Low-Quality Explanations
by: Stradiotti, Luca, et al.
Published: (2025)

Revisiting Meta-Learning with Noisy Labels: Reweighting Dynamics and Theoretical Guarantees
by: Zhang, Yiming, et al.
Published: (2025)

Differentiable Extensions with Rounding Guarantees for Combinatorial Optimization over Permutations
by: Nerem, Robert R., et al.
Published: (2024)

Unsupervised Feature Selection Through Group Discovery
by: Lifshitz, Shira, et al.
Published: (2025)

Multi-Integration of Labels across Categories for Component Identification (MILCCI)
by: Mudrik, Noga, et al.
Published: (2026)

Joint Hierarchical Representation Learning of Samples and Features via Informed Tree-Wasserstein Distance
by: Lin, Ya-Wei Eileen, et al.
Published: (2025)

Semi-Supervised Laplace Learning on Stiefel Manifolds
by: Holtz, Chester, et al.
Published: (2023)

Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy
by: Lin, Ya-Wei Eileen, et al.
Published: (2024)

Forget and Explain: Transparent Verification of GNN Unlearning
by: Ahsan, Imran, et al.
Published: (2025)

Multiway Multislice PHATE: Visualizing Hidden Dynamics of RNNs through Training
by: Xie, Jiancheng, et al.
Published: (2024)

Geometric Algorithms for Neural Combinatorial Optimization with Constraints
by: Karalias, Nikolaos, et al.
Published: (2025)

On the Consistency of GNN Explanations for Malware Detection
by: Shokouhinejad, Hossein, et al.
Published: (2025)

Graph Reconstruction from Differentially Private GNN Explanations
by: Sahoo, Rishi Raj, et al.
Published: (2026)

SliceGX: Layer-wise GNN Explanation with Model-slicing
by: Zhu, Tingting, et al.
Published: (2025)

QGShap: Quantum Acceleration for Faithful GNN Explanations
by: Jena, Haribandhu, et al.
Published: (2025)

ALIGN: Adversarial Learning for Generalizable Speech Neuroprosthesis
by: Zhang, Zhanqi, et al.
Published: (2026)

Learning Ultrametric Trees for Optimal Transport Regression
by: Chen, Samantha, et al.
Published: (2022)

Distances for Markov Chains, and Their Differentiation
by: Brugère, Tristan, et al.
Published: (2023)

Why Self-Inconsistency Arises in GNN Explanations and How to Exploit It
by: Tai, Wenxin, et al.
Published: (2026)

Efficient GNN Training Through Structure-Aware Randomized Mini-Batching
by: Balaji, Vignesh, et al.
Published: (2025)

GNNShap: Scalable and Accurate GNN Explanation using Shapley Values
by: Akkas, Selahattin, et al.
Published: (2024)

On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
by: Hong, Yusu, et al.
Published: (2024)

Revisiting Convergence of AdaGrad with Relaxed Assumptions
by: Hong, Yusu, et al.
Published: (2024)

General and Efficient Steering of Unconditional Diffusion
by: Wang, Qingsong, et al.
Published: (2026)

Policy Gradient with Tree Expansion
by: Dalal, Gal, et al.
Published: (2023)

Coupled Transformer Autoencoder for Disentangling Multi-Region Neural Latent Dynamics
by: Sristi, Ram Dyuthi, et al.
Published: (2025)